Introduction
Background
Research model
TAM
OLC
Literature review
Concept of big data
Components of big data and applications
Volume
Velocity
Variety
Technology acceptance model (TAM)
Dimensions of TAM
Limitations of TAM
Organizational learning capabilities (OLC)
Concepts of OLC
Definition of OLC
Research model and hypotheses
No | Factor | Name | Supported by | Relevance |
---|---|---|---|---|
F1 | tamUse | Intended usage of technology | Independent variable. Refers the degree of technology usage and adoption | |
F2 | tamPeou | Perceived ease of use | Individuals are motivated to adopt a new technology when using a particular system would enhance job performance | |
F3 | tamPu | Perceived usefulness | Individuals are motivated to adopt a new technology when using a particular system would be free of a steep learning curve | |
F4 | olcMc | Managerial commitment | Support and leadership of top management to create and build knowledge within the organization can motivate the usage of new technologies | |
F5 | olds | System perspective | Understanding of the organisation with clear goals and objectives can impact on adoption of new systems and technologies | |
F6 | olcOe | Openness and experimentation | A favorable climate and structures that encourage individuals to try new ideas can motivate individuals to embrace a project without the fear of being punished or laid off in the event of failure | |
F7 | loci | Transfer and integration | The exchange and integration of knowledge across departments and functional areas can improve adoption of new systems and technology |
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H1: Perceived ease of usage is positively related to the intended usage of big data analytics.
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H2: Perceived usefulness is positively related to the intended usage of big data analytics.
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Hypothesis 3a: Managerial commitment positively moderates the relationship between perceived ease of usage and intended usage of big data technologies.
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Hypothesis 3b: Managerial commitment positively relates to the intended usage of big data analytics.
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Hypothesis 3c: Managerial commitment positively moderates the relationship between perceived usefulness and intended usage of big data technologies.
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Hypothesis 4a: System perspective positively moderates the relationship between perceived ease of usage and intended usage of big data technologies.
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Hypothesis 4b: System perspective positively relates to the intended usage of big data analytics.
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Hypothesis 4c: System perspective positively moderates the relationship between perceived usefulness and intended usage of big data technologies.
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Hypothesis 5a: Openness and experimentation positively moderates the relationship between perceived ease of usage and intended usage of big data technologies.
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Hypothesis 5b: Openness and experimentation positively relates to the intended usage of big data analytics.
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Hypothesis 5c: Openness and experimentation positively moderates the relationship between perceived usefulness and intended usage of big data technologies and the last set of hypothesis for the transfer and integration dimension is
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Hypothesis 6a: Transfer and integration positively moderates the relationship between perceived ease of usage and intended usage of big data technologies.
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Hypothesis 6b: Transfer and integration positively relates to the intended usage of big data analytics.
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Hypothesis 6c: Transfer and integration positively moderates the relationship between perceived usefulness and intended usage of big data technologies.
Methodology
Sampling and data collection
Model validation
Latent variable | Item | Estimate | Std. error | t-value | R2
|
---|---|---|---|---|---|
Intended usage of technology (CR = 0.94; AVE = 0.80) | tamUse1 | 1.690 | 0.075 | 22.470* | 0.837 |
tamUse2 | 1.799 | 0.073 | 24.562* | 0.926 | |
tamUse3 | 1.475 | 0.084 | 17.657* | 0.618 | |
tamUse5 | 1.676 | 0.077 | 21.744* | 0.806 | |
Perceived ease of use (CR = 0.90; AVE = 0.69) | tamPeou2 | 0.953 | 0.056 | 17.032* | 0.605 |
tamPeou3 | 1.059 | 0.051 | 20.591* | 0.777 | |
tamPeou4 | 0.989 | 0.049 | 20.325* | 0.764 | |
tamPeou5 | 0.904 | 0.053 | 17.151* | 0.611 | |
Perceived usefulness (CR = 0.96; AVE = 0.85) | tamPu2 | 1.433 | 0.061 | 23.611* | 0.882 |
tamPu3 | 1.443 | 0.060 | 24.005* | 0.899 | |
tamPu4 | 1.383 | 0.058 | 23.638* | 0.883 | |
tamPu6 | 1.192 | 0.060 | 19.969* | 0.724 |
Latent variable | Item | Estimate | Std. error | t-value | R2
|
---|---|---|---|---|---|
Managerial commitment (CR = 0.78; AVE = 0.64) | olcMc2 | 1.119 | 0.071 | 15.843* | 0.570 |
olcMc3 | 1.313 | 0.072 | 18.129* | 0.705 | |
System perspective (CR = 0.87; AVE = 0.68) | olcSp2 | 1.132 | 0.062 | 18.200* | 0.672 |
olcSp3 | 1.305 | 0.067 | 19.418* | 0.732 | |
olcSp4 | 1.199 | 0.068 | 17.636* | 0.644 | |
Openness and experimentation (CR = 0.84; AVE = 0.73) | olcOe1 | 1.312 | 0.066 | 20.000* | 0.751 |
olcOe4 | 1.184 | 0.062 | 19.028* | 0.701 | |
Transfer and integration (CR = 0.89; AVE = 0.67) | olcTi1 | 1.222 | 0.071 | 17.237* | 0.609 |
olcTi2 | 1.325 | 0.066 | 20.045* | 0.741 | |
olcTi3 | 1.191 | 0.061 | 19.415* | 0.712 | |
olcTi4 | 1.260 | 0.073 | 17.337* | 0.614 |
Data analysis
Descriptive statistics
Program | Freq. | % | Cumul. % |
---|---|---|---|
M.Sc. in computer security/computer information security | 53 | 14.76 | 14.76 |
M.Sc. in information systems and technology | 35 | 9.75 | 24.51 |
M.Sc. in information systems management | 123 | 34.26 | 58.77 |
M.Sc. in information systems project management | 24 | 6.69 | 65.46 |
M.Sc. in information technology | 57 | 15.88 | 81.34 |
M.Sc. in internet systems | 14 | 3.90 | 85.24 |
M.Sc. in software engineering | 45 | 12.53 | 97.77 |
M.Sc. in web sciences and big data | 5 | 1.39 | 99.16 |
PG Cert in information systems and technology | 3 | 0. | 100.00 |
Total | 359 | 100.0 | – |
Age (years) | Freq. | Percentage |
---|---|---|
≤25 | 8 | 2.23 |
26–30 | 69 | 19.22 |
31–35 | 118 | 32.87 |
36–40 | 87 | 24.23 |
41–50 | 69 | 19.22 |
51 and over | 8 | 2.23 |
Total | 359 | 100 |
Inferential statistics, hypotheses tests and discussion
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. tamUse | – | |||||||||||||
2. tamPeou | 0.46*** | – | ||||||||||||
3. tamPu | 0.53*** | 0.59*** | – | |||||||||||
4. olcMc | 0.35*** | 0.21*** | 0.18*** | – | ||||||||||
5. olcSp | 0.31*** | 0.20*** | 0.14** | 0.63*** | – | |||||||||
6. olcOe | 0.31*** | 0.22*** | 0.18*** | 0.73*** | 0.73*** | – | ||||||||
7. olcTi | 0.33*** | 0.19*** | 0.13* | 0.73*** | 0.72*** | 0.82*** | – | |||||||
8. contSize | 0.16** | 0.01 | 0.06 | 0.14** | 0.08 | 0.09 | 0.15** | – | ||||||
9. contSizeIt | 0.13* | −0.06 | −0.06 | 0.19*** | 0.08 | 0.16** | 0.21*** | 0.75*** | – | |||||
10. contExp1 | 0.04 | 0.02 | 0.00 | −0.01 | 0.06 | 0.03 | −0.01 | −0.15** | −0.14** | – | ||||
11. contExp2 | 0.08 | 0.03 | 0.05 | 0.05 | 0.07 | 0.03 | 0.01 | −0.02 | −0.06 | 0.25*** | – | |||
12. contAge | 0.04 | −0.06 | 0.00 | 0.08 | 0.07 | 0.08 | 0.03 | 0.10+
| 0.05 | 0.24*** | 0.21*** | – | ||
13. contIntr | 0.05 | −0.02 | −0.05 | 0.19*** | 0.06 | 0.09+
| 0.14** | 0.21*** | 0.23*** | −0.02 | −0.13* | 0.01 | – | |
14. contInvs | 0.08 | −0.05 | 0.00 | 0.06 | 0.05 | 0.09 | 0.10+
| 0.00 | 0.13* | −0.01 | −0.06 | 0.04 | 0.03 | – |
Mean | 3.80 | 4.78 | 4.96 | 5.12 | 4.96 | 5.01 | 4.98 | 6.64 | 3.85 | 4.99 | 6.68 | 35.88 | 15.08 | 38.18 |
SD | 1.71 | 1.03 | 1.39 | 1.38 | 1.30 | 1.36 | 1.33 | 2.36 | 2.22 | 3.52 | 7.77 | 7.54 | 17.74 | 35.91 |
Scale | 1:7 | 1:7 | 1:7 | 1:7 | 1:7 | 1:7 | 1:7 | log(n) | log(n) | 1:n | 1:n | 1:n | % | % |
Regression and moderation test
Model 1 | Model 2 | Model 3 | Model 4 | |
---|---|---|---|---|
(Intercept) | 2.45*** | −1.7** | −1.47* | −1.21 |
contSize | 0.15* | 0.06 | 0.08 | 0.09+
|
contSizeIt | −0.06 | 0.04 | −0.01 | −0.01 |
contExp1 | 0.03 | 0.02 | 0.02 | 0.02 |
contExp2 | 0.02+
| 0.01 | 0.01 | 0.01 |
contInds-education and research | −0.01 | −0.02 | −0.04 | 0.04 |
contInds-energy and utilities | 0.2 | 0.04 | −0.01 | 0.09 |
contInds-government | −0.02 | −0.24 | −0.07 | −0.03 |
contInds-healthcare | 0.23 | 0.2 | 0.12 | 0.2 |
contInds-information technology | 0.5 | 0.32 | 0.35 | 0.44 |
contInds-insurance | 0.38 | 0.14 | 0.02 | 0.07 |
contInds-investment | 0.89 | 0.62 | 0.64 | 0.66 |
contInds-manufacturing | −0.26 | −0.21 | −0.22 | −0.06 |
contInds-professional service | −0.06 | −0.39 | −0.4 | −0.38 |
contInds-retail | −0.57 | −0.43 | −0.26 | −0.14 |
contInds-retail banking | 0.76+
| 0.52 | 0.43 | 0.5 |
contInds-telecoms | 0.3 | 0.09 | 0.19 | 0.24 |
contInds-transport and logistics | −0.67 | −0.94 | −0.49 | −0.46 |
contCour-M.Sc. in info sys and tech | 0.08 | 0.04 | −0.1 | −0.02 |
contCour-M.Sc. in info sys management | 0.05 | 0.04 | 0.06 | 0.08 |
contCour-M.Sc. in info sys project man | 0.58 | 0.41 | 0.55 | 0.53 |
contCour-M.Sc in info technology | −0.03 | 0.24 | 0.18 | 0.21 |
contCour-M.Sc. in internet systems | −0.7 | −0.28 | −0.32 | −0.29 |
contCour-M.Sc. in software Engineering | −0.16 | 0.18 | 0.11 | 0.09 |
contCour-M.Sc. in web sci and big data | 0.41 | −0.01 | 0.21 | 0.04 |
contCour-PGCert in Info Sys and Tech | 1.43 | 0.42 | 0.3 | 0.33 |
contIntr | 0 | 0 | 0 | 0 |
contInvs | 0.01+
| 0.01* | 0.01 | 0.01 |
contAge | 0 | 0 | 0 | 0 |
tamPu | 0.5*** | 0.47*** | 0.46*** | |
tamPeou | 0.37*** | 0.29** | 0.21 | |
olcMcDic | 0.41* | 0.65 | ||
olcSpDic | 0.27 | −0.37 | ||
olcOeDic | 0.07 | −1.05 | ||
olcTiDic | 0.19 | 0.78 | ||
tamPu:olcMcDic | −0.18 | |||
tamPu:olcSpDic | −0.27 | |||
tamPu:olcOeDic | −0.11 | |||
tamPu:olcTiDic | 0.59* | |||
tamPeou:olcMcDic | 0.14 | |||
tamPeou:olcSpDic | 0.42* | |||
tamPeou:olcOeDic | 0.32 | |||
tamPeou:olcTiDic | −0.74* | |||
R2
| 0.09 | 0.39 | 0.43 | 0.44 |
∆R2
| 0.3 | 0.04 | 0.02 | |
F | 1.15 | 6.91*** | 7.08*** | 5.98*** |
df | 28,330 | 30,328 | 34,324 | 42,316 |
Effect simulations and mediation test
olcMc | olcSp | olcOe | olcTi | |
---|---|---|---|---|
tamPu | ||||
ACME | 0.0218 | 0.00124 | 0.02366 | 0.00193 |
ADE | 0.6068*** | 0.62872*** | 0.61617*** | 0.62373*** |
Total effect | 0.6287*** | 0.62996*** | 0.63983*** | 0.62566*** |
Prop. Meda
| 0.0327 | 0.00216 | 0.03441 | 0.00305 |
tamPeou | ||||
ACME | 0.04643+
| 0.03663+
| 0.04914* | 0.0335 |
ADE | 0.66878*** | 0.68892*** | 0.67901*** | 0.6856*** |
Total effect | 0.71521*** | 0.72555*** | 0.72815*** | 0.7191*** |
Prop. Meda
| 0.06238+
| 0.04793+
| 0.06376* | 0.4390 |